智能车 self driving car + 强化学习 reinforcement learning + 神经网络 模拟 https://github.com/MorvanZhou/my_research/tree/master/self_driving_research_DQN Reinforcement Learning for Autonomous Driving Obstacle Avoidance using LIDAR https://github.com/peteflorence/…

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch Github:https://github.com/astooke/rlpyt Introduction (CH):https://baijiahao.baidu.com/s?id=1646437256939374418&wfr=spider&for=pc Introduction (EN):https://bair.berkeley.edu/blo…

Playing FPS games with deep reinforcement learning 博文转自:https://blog.acolyer.org/2016/11/23/playing-fps-games-with-deep-reinforcement-learning/ When I wrote up 'Asynchronous methods for deep learning' last month, I made a throwaway remark that after…

Byte Tank Posts Archive Deep Reinforcement Learning: Playing a Racing Game OCT 6TH, 2016 Agent playing Out Run, session 201609171218_175epsNo time limit, no traffic, 2X time lapse Above is the built deep Q-network (DQN) agent playing Out Run, trained…

Learning how to Active Learn: A Deep Reinforcement Learning Approach 2018-03-11 12:56:04 1. Introduction: 对于大部分 NLP 的任务,得到足够的标注文本来进行模型的训练是一个关键的瓶颈.所以,active learning 被引入到 NLP 任务中以最小化标注数据的代价.AL 的目标是通过识别一小部分数据来进行标注,以此来降低 cost,选来最小化监督模型的精度. 毫无疑问的是,AL 对于其…

Deep Reinforcement Learning Papers A list of recent papers regarding deep reinforcement learning. The papers are organized based on manually-defined bookmarks. They are sorted by time to see the recent papers first. Any suggestions and pull requests…

1. 知乎上关于DQN入门的系列文章 1.1 DQN 从入门到放弃 DQN 从入门到放弃1 DQN与增强学习 DQN 从入门到放弃2 增强学习与MDP DQN 从入门到放弃3 价值函数与Bellman方程 DQN 从入门到放弃4 动态规划与Q-Learning DQN从入门到放弃5 深度解读DQN算法 DQN从入门到放弃6 DQN的各种改进 DQN从入门到放弃7 连续控制DQN算法-NAF 12/29/2016 看完1和2: 1.2 Deep Reinforcement Learning 深度增…

Dueling Network Architectures for Deep Reinforcement Learning ICML 2016 Best Paper 摘要:本文的贡献点主要是在 DQN 网络结构上,将卷积神经网络提出的特征,分为两路走,即:the state value function 和 the state-dependent action advantage function. 这个设计的主要特色在于 generalize learning across actions w…

Apparently, this ongoing work is to make a preparation for futural research on Deep Reinforcement Learning. The goal of this work is to build a simulation platform that can insert the Deep Reinforcement Learning algorithms as a robot motion planning…

Andrej Karpathy blog About Hacker's guide to Neural Networks Deep Reinforcement Learning: Pong from Pixels May 31, 2016 This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatica…